The recent advancements in battery management systems (BMS) for Lithium-ion batteries have seen a shift towards more sophisticated models that capture the intricate dynamics of battery degradation and state estimation. Researchers are increasingly focusing on developing reduced-order models that balance computational efficiency with accuracy, particularly for real-time applications in electric vehicles. These models are integrating advanced numerical methods, such as the Finite Volume Method, to handle complex electrochemical processes and improve observability analysis. Additionally, there is a growing interest in leveraging machine learning techniques, including Long Short-Term Memory (LSTM) networks and Sparse Identification of Nonlinear Dynamics (SINDy), to enhance state-of-health (SOH) estimation under diverse operating conditions. The integration of electrode-level equivalent-circuit models (ECMs) is also gaining traction, offering more granular insights into battery degradation modes. Overall, the field is progressing towards more interpretable, efficient, and accurate BMS solutions that promise to extend battery life and enhance the reliability of electric transportation systems.

Noteworthy papers include one that successfully applies the Finite Volume Method to a Core Shell Average Enhanced Single Particle Model, significantly reducing computational load while maintaining accuracy. Another paper stands out for its use of the Distribution of Relaxation Times (DRT) technique combined with an LSTM-based neural network for precise SOH estimation under varying conditions.